
淺談利用邏輯回歸來解決文本分類時的模型調(diào)優(yōu)
想和數(shù)據(jù)挖掘沾點邊,所以最近在復習一些算法,因為又學了點R,深感這是個統(tǒng)計分析挖掘的利器,所以想用R實現(xiàn)一些挖掘算法。
樸素貝葉斯法大概是最簡單的一種挖掘算法了,《統(tǒng)計學習方法》在第四章做了很詳細的敘述,無非是對于輸入特征x,利用通過學習得到的模型計算后驗概率分布,將后驗概率最大的分類作為輸出。
根據(jù)貝葉斯定理,后驗概率P(Y=cx | X=x) = 條件概率P(X=x | Y=cx) * 先驗概率P(Y = ck) / P(X=x),取P(X=x | Y=cx) * P(Y = ck)最大的分類作為輸出。
下面是一個小數(shù)據(jù)集下使用R進行樸素貝葉斯分類的例子,代碼如下:
#構造訓練集
data <- matrix(c("sunny","hot","high","weak","no",
"sunny","hot","high","strong","no",
"overcast","hot","high","weak","yes",
"rain","mild","high","weak","yes",
"rain","cool","normal","weak","yes",
"rain","cool","normal","strong","no",
"overcast","cool","normal","strong","yes",
"sunny","mild","high","weak","no",
"sunny","cool","normal","weak","yes",
"rain","mild","normal","weak","yes",
"sunny","mild","normal","strong","yes",
"overcast","mild","high","strong","yes",
"overcast","hot","normal","weak","yes",
"rain","mild","high","strong","no"), byrow = TRUE,
dimnames = list(day = c(),
condition = c("outlook","temperature",
"humidity","wind","playtennis")), nrow=14, ncol=5);
#計算先驗概率
prior.yes = sum(data[,5] == "yes") / length(data[,5]);
prior.no = sum(data[,5] == "no") / length(data[,5]);
#模型
naive.bayes.prediction <- function(condition.vec) {
# Calculate unnormlized posterior probability for playtennis = yes.
playtennis.yes <-
sum((data[,1] == condition.vec[1]) & (data[,5] == "yes")) / sum(data[,5] == "yes") * # P(outlook = f_1 | playtennis = yes)
sum((data[,2] == condition.vec[2]) & (data[,5] == "yes")) / sum(data[,5] == "yes") * # P(temperature = f_2 | playtennis = yes)
sum((data[,3] == condition.vec[3]) & (data[,5] == "yes")) / sum(data[,5] == "yes") * # P(humidity = f_3 | playtennis = yes)
sum((data[,4] == condition.vec[4]) & (data[,5] == "yes")) / sum(data[,5] == "yes") * # P(wind = f_4 | playtennis = yes)
prior.yes; # P(playtennis = yes)
# Calculate unnormlized posterior probability for playtennis = no.
playtennis.no <-
sum((data[,1] == condition.vec[1]) & (data[,5] == "no")) / sum(data[,5] == "no") * # P(outlook = f_1 | playtennis = no)
sum((data[,2] == condition.vec[2]) & (data[,5] == "no")) / sum(data[,5] == "no") * # P(temperature = f_2 | playtennis = no)
sum((data[,3] == condition.vec[3]) & (data[,5] == "no")) / sum(data[,5] == "no") * # P(humidity = f_3 | playtennis = no)
sum((data[,4] == condition.vec[4]) & (data[,5] == "no")) / sum(data[,5] == "no") * # P(wind = f_4 | playtennis = no)
prior.no; # P(playtennis = no)
return(list(post.pr.yes = playtennis.yes,
post.pr.no = playtennis.no,
prediction = ifelse(playtennis.yes >= playtennis.no, "yes", "no")));
}
#預測
naive.bayes.prediction(c("rain", "hot", "high", "strong"));
naive.bayes.prediction(c("sunny", "mild", "normal", "weak"));
naive.bayes.prediction(c("overcast", "mild", "normal", "weak"));
最后一個分類預測結(jié)果如下:
$post.pr.yes
[1] 0.05643739
$post.pr.no
[1] 0
$prediction
[1] "yes"
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